May 18, 2019 · This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants.

The ambition of the ChaLearn AutoML challenge series is to channel the energy of the ML community to reduce step by step the need for human intervention in ...

Oct 26, 2018 · The ChaLearn AutoML Challenge1 (NIPS 2015 - ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, ...

This chapter analyzes the results of a machine learning competition of progressive difficulty, which was followed by a one-round AutoML challenge (PAKDD ...

Analysis of the AutoML Challenge Series 2015–2018. Springer Cham 2019 · Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Jair Escalante, Sergio Escalera ...

This chapter analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants. The solutions of ...

For the purpose of the AutoML challenge, all samples were merged and the data were freshly randomly split in three sets: training, validation, and test. The ...

This paper analyzes the results of these competitions and provides details about the datasets, which were not revealed to the participants.

(NIPS 2015-ICML 2016) consisted of six rounds of a machine learning competition of progressive difficulty, subject to limited computational resources. It was ...

News: Analysis paper: Analysis of the AutoML Challenge series 2015-2018 Isabelle Guyon, Lisheng Sun-Hosoya, Marc Boullé, Hugo Escalante, Sergio Escalera, ...